[PDF][PDF] Modeling Trajectories with Neural Ordinary Differential Equations.
Recent advances in location-acquisition techniques have generated massive spatial
trajectory data. Recurrent Neural Networks (RNNs) are modern tools for modeling such
trajectory data. After revisiting RNN-based methods for trajectory modeling, we expose two
common critical drawbacks in the existing uses. First, RNNs are discrete-time models that
only update the hidden states upon the arrival of new observations, which makes them an
awkward fit for learning real-world trajectories with continuous-time dynamics. Second, real …
trajectory data. Recurrent Neural Networks (RNNs) are modern tools for modeling such
trajectory data. After revisiting RNN-based methods for trajectory modeling, we expose two
common critical drawbacks in the existing uses. First, RNNs are discrete-time models that
only update the hidden states upon the arrival of new observations, which makes them an
awkward fit for learning real-world trajectories with continuous-time dynamics. Second, real …
Abstract
Recent advances in location-acquisition techniques have generated massive spatial trajectory data. Recurrent Neural Networks (RNNs) are modern tools for modeling such trajectory data. After revisiting RNN-based methods for trajectory modeling, we expose two common critical drawbacks in the existing uses. First, RNNs are discrete-time models that only update the hidden states upon the arrival of new observations, which makes them an awkward fit for learning real-world trajectories with continuous-time dynamics. Second, real-world trajectories are never perfectly accurate due to unexpected sensor noise. Most RNN-based approaches are deterministic and thereby vulnerable to such noise. To tackle these challenges, we devise a novel method entitled TrajODE for more natural modeling of trajectories. It combines the continuoustime characteristic of Neural Ordinary Differential Equations (ODE) with the robustness of stochastic latent spaces. Extensive experiments on the task of trajectory classification demonstrate the superiority of our framework against the RNN counterparts.
ijcai.org
以上显示的是最相近的搜索结果。 查看全部搜索结果